Risks from models and algorithms (Risks of robustness)
AI Safety Governance Framework
National Technical Committee 260 on Cybersecurity (TC260) (2024)
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
"As deep neural networks are normally non-linear and large in size, AI systems are susceptible to complex and changing operational environments or malicious interference and inductions, possibly leading to various problems like reduced performance and decision-making errors."(p. 6)
Other risks from National Technical Committee 260 on Cybersecurity (TC260) (2024) (25)
Risks from models and algorithms (Risks of explainability)
7.4 Lack of transparency or interpretabilityRisks from models and algorithms (Risks of bias and discrimination)
1.1 Unfair discrimination and misrepresentationRisks from models and algorithms (Risks of stealing and tampering)
2.2 AI system security vulnerabilities and attacksRisks from models and algorithms (Risks of unreliable output)
3.1 False or misleading informationRisks from models and algorithms (Risks of adversarial attack)
2.2 AI system security vulnerabilities and attacksRisks from data (Risks of illegal collection and use of data)
2.1 Compromise of privacy by leaking or correctly inferring sensitive information